Extant observational studies generally support the existence of a link between neighborhood context and health. However, estimating the causal impact of neighborhood-effects from observational data has proven to be a challenge. Omission of relevant factors may lead to overestimating the effects of neighborhoods on health while inclusion of time-varying confounders that may also be mediators (e.g., income, labor force status) may lead to underestimation. Using longitudinal data from the 1990-2007 Panel Study of Income Dynamics, this study investigates the link between neighborhood poverty and overall mortality risk. A marginal structural modeling strategy is employed to appropriately adjust for simultaneous mediating and confounding factors. To address the issue of possible upward bias from the omission of key variables, sensitivity analysis to assess the robustness of results against unobserved confounding is conducted. Compared to conventional naïve estimates, which did not reveal a link between neighborhood poverty and mortality risk, the marginal structural model estimates indicated a statistically significant increase in mortality risk with increasing neighborhood poverty. Sensitivity analysis indicated that estimates were moderately robust to omitted variable bias.